Library loading

import scanpy as sc, anndata as ad, numpy as np, pandas as pd,  scvelo as scv
from scipy import sparse
from anndata import AnnData
import warnings
import socket
import holoviews as hv
import plotly.express as px

from matplotlib import pylab
import sys
import yaml
import os
from pandas.api.types import CategoricalDtype
import plotly



import matplotlib.pyplot as plt

warnings.filterwarnings('ignore')

plotly.offline.init_notebook_mode()

%matplotlib inline

sc.settings.verbosity = 3         # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=50, facecolor='white')
pylab.rcParams['figure.figsize'] = (9, 9)
outBaseName = "04.1B_Progenitors_DA"
figDir = "./figures"

scanpy==1.8.1 anndata==0.7.6 umap==0.4.6 numpy==1.20.2 scipy==1.6.3 pandas==1.2.4 scikit-learn==0.24.2 statsmodels==0.13.1 python-igraph==0.9.8 louvain==0.7.1 pynndescent==0.5.5

Configure paths

hostRoot = "-".join(socket.gethostname().split('-')[0:2])

with open(os.path.expanduser('~')+"/paths_config.yaml", 'r') as f:
    paths = yaml.load(f, Loader=yaml.FullLoader)

#indir=paths["paths"]["indir"][hostRoot]
outdir="./outdir"
FinalLeaf="/Progenitors"
#projectBaseDir=paths["paths"]["projectBaseDir"][hostRoot]
with open("./colorMap.yaml", 'r') as f:
    colorMap = yaml.load(f, Loader=yaml.FullLoader)["uns_colors"]
colorMap

{'medial': {'color': '#CD5C5C'},
 'distal': {'color': '#FFCBCB'},
 'proximal': {'color': '#8D021F'},
 'piece1': {'color': '#281E5D'},
 'piece2': {'color': '#3779FF'},
 'piece3': {'color': '#BFD4FF'},
 'control': {'color': '#0056D1'},
 'polaroid': {'color': '#DE001E'},
 'enriched': {'color': '#DE001E'},
 'not_enriched': {'color': '#0056D1'},
 'pfc': {'color': '#DE001E'},
 'somatosensory': {'color': '#E5E4E2'},
 'temporal': {'color': '#0056D1'},
 'motor': {'color': '#37F7C8'},
 'v1': {'color': '#28F30C'},
 'parietal': {'color': '#D41FFC'}}

adataAll = sc.read_h5ad(outdir+"/3_polaroid_quickAnno.h5ad")

adataAll

AnnData object with n_obs × n_vars = 18822 × 28371
    obs: 'dataset', 'organoid', 'region', 'type', 'type_region', 'regionContrast', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'is.Stressed', 'leiden0.3', 'leidenAnno'
    var: 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'mean', 'std'
    uns: 'dataset_colors', 'dendrogram_leidenAnno', 'diffmap_evals', 'draw_graph', 'is.Stressed_colors', 'leiden', 'leiden0.3_colors', 'leidenAnno_colors', 'neighbors', 'organoid_colors', 'pca', 'rank_genes_groups', 'regionContrast_colors', 'region_colors', 'type_colors', 'umap'
    obsm: 'X_diffmap', 'X_draw_graph_fa', 'X_pca', 'X_umap'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

scv.tl.score_genes_cell_cycle(adataAll)

calculating cell cycle phase
computing score 'S_score'
    finished: added
    'S_score', score of gene set (adata.obs).
    543 total control genes are used. (0:00:00)
computing score 'G2M_score'
    finished: added
    'G2M_score', score of gene set (adata.obs).
    415 total control genes are used. (0:00:00)
-->     'S_score' and 'G2M_score', scores of cell cycle phases (adata.obs)

sc.pl.umap(adataAll, color=["leiden0.3","phase"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)

... storing 'phase' as categorical

UMAP

adataAll.obs["Progenitor"] = "Other"
adataAll.obs.loc[(adataAll.obs["leiden0.3"].isin(["5","4"])) & adataAll.obs["phase"].isin(["G1"]),"Progenitor"] = "NonCyclingProgenitor"
adataAll.obs["Progenitor"]
sc.pl.umap(adataAll, color="Progenitor", groups=["NonCyclingProgenitor"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)

... storing 'Progenitor' as categorical

go back to rawdata and clean

adata = adataAll.raw.to_adata().copy()

del adata.var["highly_variable"]
adata.obs = adataAll[adata.obs_names].obs.copy()
del adata.uns
del adata.obsm
adata = adata[adata.obs["Progenitor"] == "NonCyclingProgenitor"]

for md in [md for md in adata.obs.columns if "leiden" in md and md != "leiden0.3"]:
    
    del adata.obs[md]

adata.uns["regionContrast_colors"] = [colorMap[i]["color"] for i in adata.obs["regionContrast"].cat.categories]
adata.uns["region_colors"] = [colorMap[i]["color"] for i in adata.obs["region"].cat.categories]
adata.uns["type_colors"] = [colorMap[i]["color"] for i in adata.obs["type"].cat.categories]

Trying to set attribute `.uns` of view, copying.

Sankey of leiden per type

obsTupleToMap = ("region","leiden0.3")
SankeyDF=adata.obs[list(obsTupleToMap)]
SankeyDF.columns = ["region","leiden0.3"]
SankeyDF = SankeyDF.groupby(['region','leiden0.3']).size().reset_index().rename(columns={0:'count'})
SankeyDF=SankeyDF[SankeyDF["count"] != 0]
hv.extension('bokeh')



sankey1 = hv.Sankey(SankeyDF, kdims=["region", "leiden0.3"], vdims="count")



sankey1.opts(cmap='Colorblind',label_position='left',
edge_color='region', edge_line_width=0,
node_alpha=1.0, node_width=40, node_sort=True,
width=900, height=700, bgcolor="snow")

Re HVGs

Vertical intersection

# Given the epxloratory phase as many genes were retained
VERTICAL_HVGs = {}
for group in adata.obs.type_region.unique():
    adata_group = adata[adata.obs.type_region == group].copy()
    HVGsDF = sc.pp.highly_variable_genes(adata_group, min_mean=0.0125, max_mean=3, min_disp=0.5, inplace=False, batch_key="organoid")
    VERTICAL_HVGs[group]  = set(HVGsDF[HVGsDF["highly_variable_nbatches"] == 3].index)
    
VERTICAL_HVGs = set.union(*list(VERTICAL_HVGs.values()))

extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)

len(VERTICAL_HVGs)

2237

Horizontal intersection

import itertools

# Setting up contrasts
proximal = ["polaroid1_proximal","polaroid2_proximal","polaroid3_proximal"]
medial = ["polaroid1_medial","polaroid2_medial","polaroid3_medial"]
distal = ["polaroid1_distal","polaroid2_distal","polaroid3_distal"]

p1 = ["control1_piece1","control2_piece1","control3_piece1"]
p2 = ["control1_piece2","control2_piece2","control3_piece2"]
p3 = ["control1_piece3","control2_piece3","control3_piece3"]

proximal_vs_medial  = list(itertools.product(proximal, medial))
medial_vs_distal  = list(itertools.product(medial, distal))

p1_vs_p2 = list(itertools.product(p1, p2))
p2_vs_p3 = list(itertools.product(p2, p3))

HORIZONTAL_HVGs = {}


# Proximal vs distal regions
# Proximal vs distal regions
# Proximal vs distal regions

proximal_vs_medial_HVGs = {}

for contrast in proximal_vs_medial:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    proximal_vs_medial_HVGs["_".join(contrast)] = HVGs

proximal_vs_medial_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(proximal_vs_medial_HVGs.values())])))
proximal_vs_medial_HVGs = set(proximal_vs_medial_HVGs.value_counts()[proximal_vs_medial_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["proximal_vs_medial_HVGs"] = proximal_vs_medial_HVGs

    
# medial vs distal regions
# medial vs distal regions
# medial vs distal regions

medial_vs_distal_HVGs = {}

for contrast in medial_vs_distal:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    
    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    medial_vs_distal_HVGs["_".join(contrast)] = HVGs

medial_vs_distal_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(medial_vs_distal_HVGs.values())])))
medial_vs_distal_HVGs = set(medial_vs_distal_HVGs.value_counts()[medial_vs_distal_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["medial_vs_distal_HVGs"] = medial_vs_distal_HVGs


# P1 vs P2
# P1 vs P2
# P1 vs P2

p1_vs_p2_HVGs = {}

for contrast in p1_vs_p2:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())
    
    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    p1_vs_p2_HVGs["_".join(contrast)] = HVGs

p1_vs_p2_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p1_vs_p2_HVGs.values())])))
p1_vs_p2_HVGs = set(p1_vs_p2_HVGs.value_counts()[p1_vs_p2_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p1_vs_p2_HVGs"] = p1_vs_p2_HVGs



# P2 vs P3
# P2 vs P3
# P2 vs P3

p2_vs_p3_HVGs = {}

for contrast in p2_vs_p3:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    p2_vs_p3_HVGs["_".join(contrast)] = HVGs

p2_vs_p3_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p2_vs_p3_HVGs.values())])))
p2_vs_p3_HVGs = set(p2_vs_p3_HVGs.value_counts()[p2_vs_p3_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p2_vs_p3_HVGs"] = p2_vs_p3_HVGs

polaroid1_proximal    401
polaroid1_medial       65
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_proximal    401
polaroid2_medial       31
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_proximal    401
polaroid3_medial        7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid1_medial       65
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid2_medial       31
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid3_medial        7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    183
polaroid1_medial       65
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    183
polaroid2_medial       31
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    183
polaroid3_medial        7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    111
polaroid1_medial     65
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_medial    65
polaroid2_distal    29
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_medial    65
polaroid3_distal    17
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    111
polaroid2_medial     31
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_medial    31
polaroid2_distal    29
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_medial    31
polaroid3_distal    17
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    111
polaroid3_medial      7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_distal    29
polaroid3_medial     7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_distal    17
polaroid3_medial     7
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece2    47
control1_piece1    32
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    73
control1_piece1    32
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    124
control1_piece1     32
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece1    54
control1_piece2    47
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    73
control2_piece1    54
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    124
control2_piece1     54
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece1    133
control1_piece2     47
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece1    133
control2_piece2     73
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece1    133
control3_piece2    124
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece3    55
control1_piece2    47
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece2    47
control2_piece3    41
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece3    139
control1_piece2     47
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    73
control1_piece3    55
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    73
control2_piece3    41
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece3    139
control2_piece2     73
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    124
control1_piece3     55
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    124
control2_piece3     41
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece3    139
control3_piece2    124
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)

Combine all HVGs

HORIZONTAL_HVGs = set.union(*list(HORIZONTAL_HVGs.values()))

JointHVGs = list(HORIZONTAL_HVGs.union(VERTICAL_HVGs))

adata.var["highly_variable"] = adata.var_names.isin(JointHVGs)

adata.var["highly_variable"].sum()

2250

Dataset Preprocessing

Neighbors

sc.pp.pca(adata, use_highly_variable=True)

computing PCA
    on highly variable genes
    with n_comps=50
    finished (0:00:01)

sc.pp.neighbors(adata, n_neighbors=50, n_pcs=10)

computing neighbors
    using 'X_pca' with n_pcs = 10
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:01)

sc.tl.leiden(adata, resolution=1, key_added="subLeiden")

running Leiden clustering
    finished: found 11 clusters and added
    'subLeiden', the cluster labels (adata.obs, categorical) (0:00:00)

sc.tl.umap(adata)

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:04)

sc.tl.draw_graph(adata, n_jobs=4)

drawing single-cell graph using layout 'fa'
    finished: added
    'X_draw_graph_fa', graph_drawing coordinates (adata.obsm) (0:00:08)

sc.tl.diffmap(adata)

computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
    finished (0:00:00)
    eigenvalues of transition matrix
    [1.         0.99094236 0.96714073 0.9477798  0.92777807 0.9224269
     0.91350514 0.90595865 0.90286225 0.8806806  0.86221296 0.8587253
     0.85233545 0.83723557 0.8283667 ]
    finished: added
    'X_diffmap', diffmap coordinates (adata.obsm)
    'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)

sc.pl.umap(adata, color=["organoid","type","dataset","subLeiden","MKI67"], size = 100, add_outline = True,outline_width=(0.2, 0.05), ncols=3)

sc.pl.diffmap(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)

sc.pl.draw_graph(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)

sc.pl.draw_graphper type

sc.tl.embedding_density(adata, basis='umap', groupby='type')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_type')

computing density on 'umap'
--> added
    'umap_density_type', densities (adata.obs)
    'umap_density_type_params', parameter (adata.uns)

Plot per region

sc.tl.embedding_density(adata, basis='umap', groupby='region')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_region')

computing density on 'umap'
--> added
    'umap_density_region', densities (adata.obs)
    'umap_density_region_params', parameter (adata.uns)

Plot per organoid

sc.tl.embedding_density(adata, basis='umap', groupby='organoid')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_organoid')

computing density on 'umap'
--> added
    'umap_density_organoid', densities (adata.obs)
    'umap_density_organoid_params', parameter (adata.uns)

adata.write_h5ad(outdir+FinalLeaf+"/4B_Progenitors_DA.h5ad")